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An increasing age of respondents and cognitive impairment are usual suspects for increasing difficulties in survey interviews and a decreasing data quality. This is why survey researchers tend to label residents in retirement and nursing homes as hard-to-interview and exclude them from most social surveys. In this article, I examine to what extent this label is justified and whether quality of data collected among residents in institutions for the elderly really differs from data collected within private households. For this purpose, I analyze the response behavior and quality indicators in three waves of Survey of Health, Ageing and Retirement in Europe. To control for confounding variables, I use propensity score matching to identify respondents in private households who share similar characteristics with institutionalized residents. My results confirm that most indicators of response behavior and data quality are worse in institutions compared to private households. However, when controlling for sociodemographic and health-related variables, differences get very small. These results suggest the importance of health for the data quality irrespective of the housing situation.

Die überaus heterogene Bevölkerung in Gemeinschaftsunterkünften findet in Umfragen und sogar in der amtlichen Statistik häufig keinerlei Beachtung. In diesem Artikel werden Daten zweier Wellen des Mikrozensus zur Beschreibung der Bevölkerung in Anstalten verwendet. Zunächst werden Ergebnisse aus der Erhebung des Jahres 1982 dargestellt, das letzte Jahr in dem in Deutschland die konkreten Anstaltstypen veröffentlicht wurden. Diesen Daten werden aktuelle Daten des Mikrozensus aus dem Jahr 2014 gegenübergestellt. Der Vergleich ermöglicht die Feststellung von Konstanz und Veränderungen der Bevölkerung in Gemeinschaftsunterkünften über die Zeit. Die Frage, ob diese Bevölkerung letztlich vernachlässigbar ist, soll über den Vergleich mit der Bevölkerung in Privathaushalten beantwortet werden.

In most social surveys, the elderly institutionalized population is not part of the target population because it
is considered as hard-to-reach and hard-to-interview. The deliberate exclusion of institutionalized elderly
from survey samples might cause bias, like previous studies investigating institutionalized elderly persons
and their transition to institutions implied. We use a Monte Carlo simulation based on cross-national
samples of the Survey of Health, Ageing and Retirement in Europe (SHARE) to test whether the
noncoverage and undercoverage of the elderly institutionalized population lead to biased estimates.
Moreover, we examined to what extent weights could be used to correct for the underrepresentation of the
institutionalized population. Our results show that noncoverage leads to biased estimates in two healthrelated
variables. With respect to undercoverage, the precision of all estimates is better, especially if
weights accounting for the hard-to-survey population are applied.

This report provides for the first time a detailed quantitative description based on survey data of those doctoral researchers who work and perform their research at one of the 91 Leibniz Institutes and Research Museums. In November 2017, the Leibniz PhD Network sent out invitations via PhD representatives and works councils to the doctoral researchers within the Leibniz Institutes and Leibniz Research Museums to participate in the survey's online questionnaire. The present report and the underlying survey are the products of a collaborative process within the Survey Working Group of the Leibniz PhD Network.